fix: fix minor linting errors

litellm/llms/anthropic/completion/transformation.py

@@ -55,9 +55,9 @@ class AnthropicTextConfig(BaseConfig):
     to pass metadata to anthropic, it's {"user_id": "any-relevant-information"}
     """
 
-    max_tokens_to_sample: Optional[
-        int
-    ] = litellm.max_tokens  # anthropic requires a default
+    max_tokens_to_sample: Optional[int] = (
+        litellm.max_tokens
+    )  # anthropic requires a default
     stop_sequences: Optional[list] = None
     temperature: Optional[int] = None
     top_p: Optional[int] = None
@@ -291,7 +291,7 @@ class AnthropicTextCompletionResponseIterator(BaseModelResponseIterator):
             _chunk_text = chunk.get("completion", None)
             if _chunk_text is not None and isinstance(_chunk_text, str):
                 text = _chunk_text
-            finish_reason = chunk.get("stop_reason", None)
+            finish_reason = chunk.get("stop_reason") or ""
             if finish_reason is not None:
                 is_finished = True
             returned_chunk = GenericStreamingChunk(

litellm/llms/anthropic/cost_calculation.py

@@ -49,7 +49,7 @@ def get_cost_for_anthropic_web_search(
 
     ## Get the cost per web search request
     search_context_pricing: SearchContextCostPerQuery = (
-        model_info.get("search_context_cost_per_query", {}) or {}
+        model_info.get("search_context_cost_per_query") or SearchContextCostPerQuery()
     )
     cost_per_web_search_request = search_context_pricing.get(
         "search_context_size_medium", 0.0

litellm/llms/bedrock/chat/invoke_handler.py

@@ -7,7 +7,6 @@ import json
 import time
 import types
 import urllib.parse
-from litellm._uuid import uuid
 from functools import partial
 from typing import (
     Any,
@@ -26,6 +25,7 @@ import httpx  # type: ignore
 
 import litellm
 from litellm import verbose_logger
+from litellm._uuid import uuid
 from litellm.caching.caching import InMemoryCache
 from litellm.litellm_core_utils.core_helpers import map_finish_reason
 from litellm.litellm_core_utils.litellm_logging import Logging
@@ -498,9 +498,9 @@ class BedrockLLM(BaseAWSLLM):
                             content=None,
                         )
                         model_response.choices[0].message = _message  # type: ignore
-                        model_response._hidden_params[
-                            "original_response"
-                        ] = outputText  # allow user to access raw anthropic tool calling response
+                        model_response._hidden_params["original_response"] = (
+                            outputText  # allow user to access raw anthropic tool calling response
+                        )
                     if (
                         _is_function_call is True
                         and stream is not None
@@ -808,9 +808,9 @@ class BedrockLLM(BaseAWSLLM):
                     ):  # completion(top_k=3) > anthropic_config(top_k=3) <- allows for dynamic variables to be passed in
                         inference_params[k] = v
                 if stream is True:
-                    inference_params[
-                        "stream"
-                    ] = True  # cohere requires stream = True in inference params
+                    inference_params["stream"] = (
+                        True  # cohere requires stream = True in inference params
+                    )
                 data = json.dumps({"prompt": prompt, **inference_params})
         elif provider == "anthropic":
             if model.startswith("anthropic.claude-3"):
@@ -1352,9 +1352,11 @@ class AWSEventStreamDecoder:
                             "name": None,
                             "arguments": delta_obj["toolUse"]["input"],
                         },
-                        "index": self.tool_calls_index
-                        if self.tool_calls_index is not None
-                        else index,
+                        "index": (
+                            self.tool_calls_index
+                            if self.tool_calls_index is not None
+                            else index
+                        ),
                     }
                 elif "reasoningContent" in delta_obj:
                     provider_specific_fields = {
@@ -1384,9 +1386,11 @@ class AWSEventStreamDecoder:
                             "name": None,
                             "arguments": "{}",
                         },
-                        "index": self.tool_calls_index
-                        if self.tool_calls_index is not None
-                        else index,
+                        "index": (
+                            self.tool_calls_index
+                            if self.tool_calls_index is not None
+                            else index
+                        ),
                     }
             elif "stopReason" in chunk_data:
                 finish_reason = map_finish_reason(chunk_data.get("stopReason", "stop"))
@@ -1448,7 +1452,7 @@ class AWSEventStreamDecoder:
         ######### /bedrock/invoke nova mappings ###############
         elif "contentBlockDelta" in chunk_data:
             # when using /bedrock/invoke/nova, the chunk_data is nested under "contentBlockDelta"
-            _chunk_data = chunk_data.get("contentBlockDelta", None)
+            _chunk_data = chunk_data.get("contentBlockDelta", {})
             return self.converse_chunk_parser(chunk_data=_chunk_data)
         ######## bedrock.mistral mappings ###############
         elif "outputs" in chunk_data:

litellm/llms/huggingface/embedding/transformation.py

@@ -40,17 +40,17 @@ class HuggingFaceEmbeddingConfig(BaseConfig):
     Reference: https://huggingface.github.io/text-generation-inference/#/Text%20Generation%20Inference/compat_generate
     """
 
-    hf_task: Optional[
-        hf_tasks
-    ] = None  # litellm-specific param, used to know the api spec to use when calling huggingface api
+    hf_task: Optional[hf_tasks] = (
+        None  # litellm-specific param, used to know the api spec to use when calling huggingface api
+    )
     best_of: Optional[int] = None
     decoder_input_details: Optional[bool] = None
     details: Optional[bool] = True  # enables returning logprobs + best of
     max_new_tokens: Optional[int] = None
     repetition_penalty: Optional[float] = None
-    return_full_text: Optional[
-        bool
-    ] = False  # by default don't return the input as part of the output
+    return_full_text: Optional[bool] = (
+        False  # by default don't return the input as part of the output
+    )
     seed: Optional[int] = None
     temperature: Optional[float] = None
     top_k: Optional[int] = None
@@ -120,9 +120,9 @@ class HuggingFaceEmbeddingConfig(BaseConfig):
                 optional_params["top_p"] = value
             if param == "n":
                 optional_params["best_of"] = value
-                optional_params[
-                    "do_sample"
-                ] = True  # Need to sample if you want best of for hf inference endpoints
+                optional_params["do_sample"] = (
+                    True  # Need to sample if you want best of for hf inference endpoints
+                )
             if param == "stream":
                 optional_params["stream"] = value
             if param == "stop":
@@ -268,7 +268,7 @@ class HuggingFaceEmbeddingConfig(BaseConfig):
                 # check if the model has a registered custom prompt
                 model_prompt_details = litellm.custom_prompt_dict[model]
                 prompt = custom_prompt(
-                    role_dict=model_prompt_details.get("roles", None),
+                    role_dict=model_prompt_details.get("roles") or {},
                     initial_prompt_value=model_prompt_details.get(
                         "initial_prompt_value", ""
                     ),
@@ -363,9 +363,9 @@ class HuggingFaceEmbeddingConfig(BaseConfig):
             "content-type": "application/json",
         }
         if api_key is not None:
-            default_headers[
-                "Authorization"
-            ] = f"Bearer {api_key}"  # Huggingface Inference Endpoint default is to accept bearer tokens
+            default_headers["Authorization"] = (
+                f"Bearer {api_key}"  # Huggingface Inference Endpoint default is to accept bearer tokens
+            )
 
         headers = {**headers, **default_headers}
         return headers